lowerCamelCase__  =					{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowerCamelCase__  =					{value: key for key, value in encode_dict.items()}


def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    ""
  for letter in word.lower():
    if letter.isalpha() or letter == " ":
      encoded += encode_dict[letter]
    else:
      raise Exception("encode() accepts only letters of the alphabet and spaces" )
  return encoded


def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  if set(a__ ) - {"A", "B", " "} != set():
    raise Exception("decode() accepts only 'A', 'B' and spaces" )
  _UpperCamelCase           =    ""
  for word in coded.split():
    while len(a__ ) != 0:
      decoded += decode_dict[word[:5]]
      _UpperCamelCase           =    word[5:]
    decoded += " "
  return decoded.strip()


if __name__ == "__main__":
   from doctest import testmod

   testmod()





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




